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Kwanta Assortment

Category rules, at every storefront.

The AI groups your stores, watches your SKU mix, and flags the laggards and the gaps — with the margin and stock-turn trade-offs attached. Your category leads approve; the live view updates.

  • Store-cluster roles + assortment width
  • SKU-level ABC / XYZ + lifecycle stage
  • Rationalization with margin × turn trade-offs

One weekly category review cycle replaces the quarterly "full reset" that nobody finishes.

01

Cluster the stores

Stores grouped by role (destination, convenience, outlet) and by category affinity. Each cluster gets its own assortment shape.

02

Grade the SKUs

ABC × XYZ per cluster. Lifecycle stage (launch, core, tail, markdown). Contribution to margin, turn, and customer acquisition.

03

Propose the moves

Add, remove, deepen, narrow. Each proposal comes with the expected margin delta, the turn delta, and the risk of stock-out during the change.

04

Approve and track

Category lead approves the plan. The agent tracks execution across stores, flags deviations, and reports outcomes against the proposal.

Store clustering

Auto-clustering by sales profile, catchment, footfall, category performance — with manual override per store.

ABC / XYZ

Contribution and variability per SKU, per cluster. Updated continuously, not once a quarter.

Lifecycle stage

Launch, core, tail, markdown. Each stage has its own rules for depth, facings, and pricing.

Gap analysis

Where the assortment is thin vs. demand. Where two SKUs compete for the same shelf role.

Rationalization

SKU deletions proposed with the margin and turn trade-off. Not by gut — by numbers you can check.

Plan tracking

The plan is a first-class object. Every move is tracked, flagged if delayed, reported against the original expectation.

Assortment reads from the forecast and pricing, writes back to the till and the supplier portal.

  • Kwanta POS
  • 1C
  • Shopify
  • Supplier portals
  • Looker · Metabase
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